Analyzing life expectancy vary and GDP per capita by decades

Team Work

How does life expectancy vary by continent and by decade?

data_by_decade<- function(dataset){
  dataset$decade <- cut(dataset$year, seq(1950,2010,10),labels = as.character(seq(1950,2000,10)))
  return(dataset)
}

fun_continents <- function(continent, decade) {
 
  a <- gapminder[which(gapminder$continent == continent & gapminder$decade == decade),]
  
  stats <- c(min = min(a$lifeExp), max =max( a$lifeExp),
                  mean =  mean(a$lifeExp), median = median(a$lifeExp),
                   IQR = quantile(a$lifeExp, 0.75) - quantile(a$lifeExp, 0.25))
  return(stats)
}
fun_continents( "Africa", 1950)
## Warning: Unknown or uninitialised column: 'decade'.
## Warning in min(a$lifeExp): no non-missing arguments to min; returning Inf
## Warning in max(a$lifeExp): no non-missing arguments to max; returning -Inf
##     min     max    mean  median IQR.75% 
##     Inf    -Inf     NaN      NA      NA
gapminder <- data_by_decade(gapminder)

continentdata <- c()
sta <- c()

for(continent in levels(gapminder$continent)) {
  for(decade in levels(gapminder$decade)) {
    continentdata <- rbind(continentdata, c(continent, decade))
    sta <- rbind(sta, fun_continents(continent, decade ))
  }
}
continentdata
##       [,1]       [,2]  
##  [1,] "Africa"   "1950"
##  [2,] "Africa"   "1960"
##  [3,] "Africa"   "1970"
##  [4,] "Africa"   "1980"
##  [5,] "Africa"   "1990"
##  [6,] "Africa"   "2000"
##  [7,] "Americas" "1950"
##  [8,] "Americas" "1960"
##  [9,] "Americas" "1970"
## [10,] "Americas" "1980"
## [11,] "Americas" "1990"
## [12,] "Americas" "2000"
## [13,] "Asia"     "1950"
## [14,] "Asia"     "1960"
## [15,] "Asia"     "1970"
## [16,] "Asia"     "1980"
## [17,] "Asia"     "1990"
## [18,] "Asia"     "2000"
## [19,] "Europe"   "1950"
## [20,] "Europe"   "1960"
## [21,] "Europe"   "1970"
## [22,] "Europe"   "1980"
## [23,] "Europe"   "1990"
## [24,] "Europe"   "2000"
## [25,] "Oceania"  "1950"
## [26,] "Oceania"  "1960"
## [27,] "Oceania"  "1970"
## [28,] "Oceania"  "1980"
## [29,] "Oceania"  "1990"
## [30,] "Oceania"  "2000"
sta
##          min    max     mean  median  IQR.75%
##  [1,] 30.000 58.089 40.20092 39.9890  6.51375
##  [2,] 32.767 61.557 44.32699 43.9890  7.95300
##  [3,] 35.400 67.064 48.51568 48.1205  9.14750
##  [4,] 38.445 71.913 52.46883 51.5220 11.47125
##  [5,] 23.599 74.772 53.61392 52.6000 12.32125
##  [6,] 39.193 76.442 54.06563 52.4060 11.90725
##  [7,] 37.579 69.960 54.62006 55.1540 16.43325
##  [8,] 43.428 72.130 59.40484 60.3165 12.82300
##  [9,] 46.714 74.210 63.39324 65.3720 10.41200
## [10,] 51.461 76.860 67.15978 68.1625  8.27450
## [11,] 55.089 78.610 70.35942 70.7315  5.60250
## [12,] 58.137 80.653 73.01508 72.8275  5.56150
## [13,] 28.801 67.840 47.81647 45.7770 13.72900
## [14,] 31.997 71.430 53.11343 51.6300 14.01075
## [15,] 31.220 75.380 58.46491 59.3750 12.13875
## [16,] 39.854 78.670 63.73456 64.8205 10.69550
## [17,] 41.674 80.690 67.27886 69.0085 10.77300
## [18,] 42.129 82.603 69.98118 71.6570  9.87675
## [19,] 43.585 73.470 65.55578 66.8350  7.47875
## [20,] 52.098 74.160 69.13842 70.0200  3.61550
## [21,] 57.005 76.110 71.35640 71.1400  3.17500
## [22,] 61.036 77.410 73.22428 74.0500  4.40500
## [23,] 66.146 79.390 74.97263 75.8350  4.97725
## [24,] 70.845 81.757 77.17460 78.1770  4.84150
## [25,] 69.120 70.330 69.77500 69.8250  0.95500
## [26,] 70.930 71.520 71.19750 71.1700  0.25250
## [27,] 71.890 73.490 72.38250 72.0750  0.61750
## [28,] 73.840 76.320 74.80500 74.5300  0.93500
## [29,] 76.330 78.830 77.56750 77.5550  0.63250
## [30,] 79.110 81.235 80.22975 80.2870  0.65575
s <- data.frame(continentdata, sta )
names(s)[1] <- "continent"
names(s)[2] <- "decade"
s
##    continent decade    min    max     mean  median  IQR.75.
## 1     Africa   1950 30.000 58.089 40.20092 39.9890  6.51375
## 2     Africa   1960 32.767 61.557 44.32699 43.9890  7.95300
## 3     Africa   1970 35.400 67.064 48.51568 48.1205  9.14750
## 4     Africa   1980 38.445 71.913 52.46883 51.5220 11.47125
## 5     Africa   1990 23.599 74.772 53.61392 52.6000 12.32125
## 6     Africa   2000 39.193 76.442 54.06563 52.4060 11.90725
## 7   Americas   1950 37.579 69.960 54.62006 55.1540 16.43325
## 8   Americas   1960 43.428 72.130 59.40484 60.3165 12.82300
## 9   Americas   1970 46.714 74.210 63.39324 65.3720 10.41200
## 10  Americas   1980 51.461 76.860 67.15978 68.1625  8.27450
## 11  Americas   1990 55.089 78.610 70.35942 70.7315  5.60250
## 12  Americas   2000 58.137 80.653 73.01508 72.8275  5.56150
## 13      Asia   1950 28.801 67.840 47.81647 45.7770 13.72900
## 14      Asia   1960 31.997 71.430 53.11343 51.6300 14.01075
## 15      Asia   1970 31.220 75.380 58.46491 59.3750 12.13875
## 16      Asia   1980 39.854 78.670 63.73456 64.8205 10.69550
## 17      Asia   1990 41.674 80.690 67.27886 69.0085 10.77300
## 18      Asia   2000 42.129 82.603 69.98118 71.6570  9.87675
## 19    Europe   1950 43.585 73.470 65.55578 66.8350  7.47875
## 20    Europe   1960 52.098 74.160 69.13842 70.0200  3.61550
## 21    Europe   1970 57.005 76.110 71.35640 71.1400  3.17500
## 22    Europe   1980 61.036 77.410 73.22428 74.0500  4.40500
## 23    Europe   1990 66.146 79.390 74.97263 75.8350  4.97725
## 24    Europe   2000 70.845 81.757 77.17460 78.1770  4.84150
## 25   Oceania   1950 69.120 70.330 69.77500 69.8250  0.95500
## 26   Oceania   1960 70.930 71.520 71.19750 71.1700  0.25250
## 27   Oceania   1970 71.890 73.490 72.38250 72.0750  0.61750
## 28   Oceania   1980 73.840 76.320 74.80500 74.5300  0.93500
## 29   Oceania   1990 76.330 78.830 77.56750 77.5550  0.63250
## 30   Oceania   2000 79.110 81.235 80.22975 80.2870  0.65575

From the table, we can see the life expectancy is increasing by decades for each continent. But the IQR for developing countries, like in Asia increase first and then decrease a littel bit. And for Europe and Americas, the IQR doesn’t change a lot, it’s steady.

Now, we can consider plots to look into this further.

Europe <- subset(gapminder, continent == "Europe")
Africa <- subset(gapminder, continent == "Africa")
Asia <- subset(gapminder, continent == "Asia")
Americas <- subset(gapminder, continent == "Americas")
Oceania <- subset(gapminder, continent == "Oceania")
aggregate(lifeExp~continent, gapminder, min)
##   continent lifeExp
## 1    Africa  23.599
## 2  Americas  37.579
## 3      Asia  28.801
## 4    Europe  43.585
## 5   Oceania  69.120
aggregate(lifeExp~continent, gapminder, max)
##   continent lifeExp
## 1    Africa  76.442
## 2  Americas  80.653
## 3      Asia  82.603
## 4    Europe  81.757
## 5   Oceania  81.235
aggregate(lifeExp~continent, gapminder, mean)
##   continent  lifeExp
## 1    Africa 48.86533
## 2  Americas 64.65874
## 3      Asia 60.06490
## 4    Europe 71.90369
## 5   Oceania 74.32621
aggregate(lifeExp~continent, gapminder, median)
##   continent lifeExp
## 1    Africa 47.7920
## 2  Americas 67.0480
## 3      Asia 61.7915
## 4    Europe 72.2410
## 5   Oceania 73.6650
aggregate(lifeExp~continent, gapminder, IQR)
##   continent lifeExp
## 1    Africa 12.0390
## 2  Americas 13.2895
## 3      Asia 18.0790
## 4    Europe  5.8805
## 5   Oceania  6.3475
filter(gapminder,(continent=="Europe" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Europe" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Europe" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Europe" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Europe" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Africa" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Africa" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Africa" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Africa" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Africa" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Asia" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Asia" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Asia" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Asia" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Asia" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Americas" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Americas" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Americas" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Americas" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Americas" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

How does GDP per capita vary by continent and by decade?

fun_gdp <- function(continent, decade) {
  
  a <- gapminder[which(gapminder$continent == continent & gapminder$decade == decade),]
  
  stats2 <- c(min = min(a$gdpPercap), max =max( a$gdpPercap),
             mean =  mean(a$gdpPercap), median = median(a$gdpPercap),
             IQR = quantile(a$gdpPercap, 0.75) - quantile(a$gdpPercap, 0.25))
  return(stats2)
}
fun_gdp("Africa",1950)
##       min       max      mean    median   IQR.75% 
##  298.8462 5487.1042 1318.9043 1024.0230  950.0916
gap <- data_by_decade(gapminder)

continentgdp <- c()
sta2<- c()
for(continent in levels(gap$continent)) {
  for(decade in levels(gap$decade)) {
    continentgdp <- rbind(continentgdp, c(continent, decade))
    sta2 <- rbind(sta2, fun_gdp(continent, decade ))
  }
}
continentgdp
##       [,1]       [,2]  
##  [1,] "Africa"   "1950"
##  [2,] "Africa"   "1960"
##  [3,] "Africa"   "1970"
##  [4,] "Africa"   "1980"
##  [5,] "Africa"   "1990"
##  [6,] "Africa"   "2000"
##  [7,] "Americas" "1950"
##  [8,] "Americas" "1960"
##  [9,] "Americas" "1970"
## [10,] "Americas" "1980"
## [11,] "Americas" "1990"
## [12,] "Americas" "2000"
## [13,] "Asia"     "1950"
## [14,] "Asia"     "1960"
## [15,] "Asia"     "1970"
## [16,] "Asia"     "1980"
## [17,] "Asia"     "1990"
## [18,] "Asia"     "2000"
## [19,] "Europe"   "1950"
## [20,] "Europe"   "1960"
## [21,] "Europe"   "1970"
## [22,] "Europe"   "1980"
## [23,] "Europe"   "1990"
## [24,] "Europe"   "2000"
## [25,] "Oceania"  "1950"
## [26,] "Oceania"  "1960"
## [27,] "Oceania"  "1970"
## [28,] "Oceania"  "1980"
## [29,] "Oceania"  "1990"
## [30,] "Oceania"  "2000"
sta2
##              min        max      mean    median    IQR.75%
##  [1,]   298.8462   5487.104  1318.904  1024.023   950.0916
##  [2,]   355.2032  18772.752  1824.221  1191.555  1104.8081
##  [3,]   464.0995  21951.212  2462.777  1402.376  1724.7493
##  [4,]   389.8762  17364.275  2382.131  1286.173  1983.9259
##  [5,]   312.1884  14722.842  2330.285  1179.883  2069.0063
##  [6,]   241.1659  13206.485  2844.209  1307.562  2826.6352
##  [7,]  1397.7171  14847.127  4347.553  3266.944  2205.0266
##  [8,]  1452.0577  19530.366  5284.898  4549.084  2799.6370
##  [9,]  1654.4569  24072.632  6921.671  5490.198  3124.1509
## [10,]  1823.0160  29884.350  7650.069  6397.723  4300.9379
## [11,]  1341.7269  35767.433  8467.118  6813.664  4848.2282
## [12,]  1201.6372  42951.653 10145.354  7382.469  5542.2684
## [13,]   331.0000 113523.133  5491.608  1497.727  2297.9368
## [14,]   349.0000  95458.112  5850.271  1852.401  4085.0329
## [15,]   357.0000 109347.867  7989.391  2850.723  8539.1421
## [16,]   385.0000  33693.175  7521.181  4106.509 11165.1958
## [17,]   347.0000  40300.620  9236.892  3685.722 15728.0982
## [18,]   611.0000  47306.990 11323.559  4287.633 18938.5251
## [19,]   973.5332  17909.490  6312.035  5730.677  4956.2819
## [20,]  1709.6837  22966.144  9254.655  8463.000  6811.9320
## [21,]  2860.1698  27195.113 13381.777 13044.562  8352.9485
## [22,]  3630.8807  31540.975 16416.104 15995.703  9922.0911
## [23,]  2497.4379  41283.164 18069.175 18122.048 16830.2216
## [24,]  4604.2117  49357.190 23383.107 26653.335 20705.2445
## [25,] 10039.5956  12247.395 10948.304 10753.113   846.7554
## [26,] 12217.2269  14526.125 13595.737 13819.798  1543.4051
## [27,] 16046.0373  18334.198 16850.645 16511.174   988.2239
## [28,] 17632.4104  21888.889 19501.375 19242.100  1416.4832
## [29,] 18363.3249  26997.937 22459.111 22237.590  3939.4177
## [30,] 23189.8014  34435.367 28374.483 27936.382  6938.4507
s2 <- data.frame(continentgdp,sta2)
names(s2)[1] <- "continent"
names(s2)[2] <- "decade"
s2
##    continent decade        min        max      mean    median    IQR.75.
## 1     Africa   1950   298.8462   5487.104  1318.904  1024.023   950.0916
## 2     Africa   1960   355.2032  18772.752  1824.221  1191.555  1104.8081
## 3     Africa   1970   464.0995  21951.212  2462.777  1402.376  1724.7493
## 4     Africa   1980   389.8762  17364.275  2382.131  1286.173  1983.9259
## 5     Africa   1990   312.1884  14722.842  2330.285  1179.883  2069.0063
## 6     Africa   2000   241.1659  13206.485  2844.209  1307.562  2826.6352
## 7   Americas   1950  1397.7171  14847.127  4347.553  3266.944  2205.0266
## 8   Americas   1960  1452.0577  19530.366  5284.898  4549.084  2799.6370
## 9   Americas   1970  1654.4569  24072.632  6921.671  5490.198  3124.1509
## 10  Americas   1980  1823.0160  29884.350  7650.069  6397.723  4300.9379
## 11  Americas   1990  1341.7269  35767.433  8467.118  6813.664  4848.2282
## 12  Americas   2000  1201.6372  42951.653 10145.354  7382.469  5542.2684
## 13      Asia   1950   331.0000 113523.133  5491.608  1497.727  2297.9368
## 14      Asia   1960   349.0000  95458.112  5850.271  1852.401  4085.0329
## 15      Asia   1970   357.0000 109347.867  7989.391  2850.723  8539.1421
## 16      Asia   1980   385.0000  33693.175  7521.181  4106.509 11165.1958
## 17      Asia   1990   347.0000  40300.620  9236.892  3685.722 15728.0982
## 18      Asia   2000   611.0000  47306.990 11323.559  4287.633 18938.5251
## 19    Europe   1950   973.5332  17909.490  6312.035  5730.677  4956.2819
## 20    Europe   1960  1709.6837  22966.144  9254.655  8463.000  6811.9320
## 21    Europe   1970  2860.1698  27195.113 13381.777 13044.562  8352.9485
## 22    Europe   1980  3630.8807  31540.975 16416.104 15995.703  9922.0911
## 23    Europe   1990  2497.4379  41283.164 18069.175 18122.048 16830.2216
## 24    Europe   2000  4604.2117  49357.190 23383.107 26653.335 20705.2445
## 25   Oceania   1950 10039.5956  12247.395 10948.304 10753.113   846.7554
## 26   Oceania   1960 12217.2269  14526.125 13595.737 13819.798  1543.4051
## 27   Oceania   1970 16046.0373  18334.198 16850.645 16511.174   988.2239
## 28   Oceania   1980 17632.4104  21888.889 19501.375 19242.100  1416.4832
## 29   Oceania   1990 18363.3249  26997.937 22459.111 22237.590  3939.4177
## 30   Oceania   2000 23189.8014  34435.367 28374.483 27936.382  6938.4507

The highest GDP per capita lies in developed countries, and their changes a smaller than Asia. But Asia has a great increase by decades.

Summary of Who did What

Lauren

Kaite

I used several functions to form two tables, one is for the changes of life expectancy by continents by decade, and another is the changes of GDP per capita by continent by decades. And those two tables illustrated 5 statsitics for each decade and continent. We could see the increaseing tendency of changing of life expectancy from those continents, especially Asia. And the great increasing in GDP in Asia too.

Ryan

Chris

I tried to analysis the 5 decades the life expectancy and the gdp per capita, first time I just used subset to five indiviual continent and use the aggregate function tried to calculate the answer, but the only things that i can find is the averge answer durning the whole time and I stuck in every decades, so I used another way tried to find the relation durning the plots. for each question I created 5*5 plots. 5 contients and 5 decedes, and from the graphs that I got, it is really easy to conclude that the life expectancy and the cgp per capita increase when the years increase.

Individual Work

Lauren

How does life expectancy vary by continent and by decade?

How does GDP per capita vary by continent and by decade?

Kaite

How does life expectancy vary by continent and by decade?

data_by_decade<- function(dataset){
  dataset$decade <- cut(dataset$year, seq(1950,2010,10),labels = as.character(seq(1950,2000,10)))
  return(dataset)
}

fun_continents <- function(continent, decade) {
 
  a <- gapminder[which(gapminder$continent == continent & gapminder$decade == decade),]
  
  stats <- c(min = min(a$lifeExp), max =max( a$lifeExp),
                  mean =  mean(a$lifeExp), median = median(a$lifeExp),
                   IQR = quantile(a$lifeExp, 0.75) - quantile(a$lifeExp, 0.25))
  return(stats)
}
fun_continents( "Africa", 1950)
##      min      max     mean   median  IQR.75% 
## 30.00000 58.08900 40.20092 39.98900  6.51375
gapminder <- data_by_decade(gapminder)

continentdata <- c()
sta <- c()

for(continent in levels(gapminder$continent)) {
  for(decade in levels(gapminder$decade)) {
    continentdata <- rbind(continentdata, c(continent, decade))
    sta <- rbind(sta, fun_continents(continent, decade ))
  }
}
continentdata
##       [,1]       [,2]  
##  [1,] "Africa"   "1950"
##  [2,] "Africa"   "1960"
##  [3,] "Africa"   "1970"
##  [4,] "Africa"   "1980"
##  [5,] "Africa"   "1990"
##  [6,] "Africa"   "2000"
##  [7,] "Americas" "1950"
##  [8,] "Americas" "1960"
##  [9,] "Americas" "1970"
## [10,] "Americas" "1980"
## [11,] "Americas" "1990"
## [12,] "Americas" "2000"
## [13,] "Asia"     "1950"
## [14,] "Asia"     "1960"
## [15,] "Asia"     "1970"
## [16,] "Asia"     "1980"
## [17,] "Asia"     "1990"
## [18,] "Asia"     "2000"
## [19,] "Europe"   "1950"
## [20,] "Europe"   "1960"
## [21,] "Europe"   "1970"
## [22,] "Europe"   "1980"
## [23,] "Europe"   "1990"
## [24,] "Europe"   "2000"
## [25,] "Oceania"  "1950"
## [26,] "Oceania"  "1960"
## [27,] "Oceania"  "1970"
## [28,] "Oceania"  "1980"
## [29,] "Oceania"  "1990"
## [30,] "Oceania"  "2000"
sta
##          min    max     mean  median  IQR.75%
##  [1,] 30.000 58.089 40.20092 39.9890  6.51375
##  [2,] 32.767 61.557 44.32699 43.9890  7.95300
##  [3,] 35.400 67.064 48.51568 48.1205  9.14750
##  [4,] 38.445 71.913 52.46883 51.5220 11.47125
##  [5,] 23.599 74.772 53.61392 52.6000 12.32125
##  [6,] 39.193 76.442 54.06563 52.4060 11.90725
##  [7,] 37.579 69.960 54.62006 55.1540 16.43325
##  [8,] 43.428 72.130 59.40484 60.3165 12.82300
##  [9,] 46.714 74.210 63.39324 65.3720 10.41200
## [10,] 51.461 76.860 67.15978 68.1625  8.27450
## [11,] 55.089 78.610 70.35942 70.7315  5.60250
## [12,] 58.137 80.653 73.01508 72.8275  5.56150
## [13,] 28.801 67.840 47.81647 45.7770 13.72900
## [14,] 31.997 71.430 53.11343 51.6300 14.01075
## [15,] 31.220 75.380 58.46491 59.3750 12.13875
## [16,] 39.854 78.670 63.73456 64.8205 10.69550
## [17,] 41.674 80.690 67.27886 69.0085 10.77300
## [18,] 42.129 82.603 69.98118 71.6570  9.87675
## [19,] 43.585 73.470 65.55578 66.8350  7.47875
## [20,] 52.098 74.160 69.13842 70.0200  3.61550
## [21,] 57.005 76.110 71.35640 71.1400  3.17500
## [22,] 61.036 77.410 73.22428 74.0500  4.40500
## [23,] 66.146 79.390 74.97263 75.8350  4.97725
## [24,] 70.845 81.757 77.17460 78.1770  4.84150
## [25,] 69.120 70.330 69.77500 69.8250  0.95500
## [26,] 70.930 71.520 71.19750 71.1700  0.25250
## [27,] 71.890 73.490 72.38250 72.0750  0.61750
## [28,] 73.840 76.320 74.80500 74.5300  0.93500
## [29,] 76.330 78.830 77.56750 77.5550  0.63250
## [30,] 79.110 81.235 80.22975 80.2870  0.65575
s <- data.frame(continentdata, sta )
names(s)[1] <- "continent"
names(s)[2] <- "decade"
s
##    continent decade    min    max     mean  median  IQR.75.
## 1     Africa   1950 30.000 58.089 40.20092 39.9890  6.51375
## 2     Africa   1960 32.767 61.557 44.32699 43.9890  7.95300
## 3     Africa   1970 35.400 67.064 48.51568 48.1205  9.14750
## 4     Africa   1980 38.445 71.913 52.46883 51.5220 11.47125
## 5     Africa   1990 23.599 74.772 53.61392 52.6000 12.32125
## 6     Africa   2000 39.193 76.442 54.06563 52.4060 11.90725
## 7   Americas   1950 37.579 69.960 54.62006 55.1540 16.43325
## 8   Americas   1960 43.428 72.130 59.40484 60.3165 12.82300
## 9   Americas   1970 46.714 74.210 63.39324 65.3720 10.41200
## 10  Americas   1980 51.461 76.860 67.15978 68.1625  8.27450
## 11  Americas   1990 55.089 78.610 70.35942 70.7315  5.60250
## 12  Americas   2000 58.137 80.653 73.01508 72.8275  5.56150
## 13      Asia   1950 28.801 67.840 47.81647 45.7770 13.72900
## 14      Asia   1960 31.997 71.430 53.11343 51.6300 14.01075
## 15      Asia   1970 31.220 75.380 58.46491 59.3750 12.13875
## 16      Asia   1980 39.854 78.670 63.73456 64.8205 10.69550
## 17      Asia   1990 41.674 80.690 67.27886 69.0085 10.77300
## 18      Asia   2000 42.129 82.603 69.98118 71.6570  9.87675
## 19    Europe   1950 43.585 73.470 65.55578 66.8350  7.47875
## 20    Europe   1960 52.098 74.160 69.13842 70.0200  3.61550
## 21    Europe   1970 57.005 76.110 71.35640 71.1400  3.17500
## 22    Europe   1980 61.036 77.410 73.22428 74.0500  4.40500
## 23    Europe   1990 66.146 79.390 74.97263 75.8350  4.97725
## 24    Europe   2000 70.845 81.757 77.17460 78.1770  4.84150
## 25   Oceania   1950 69.120 70.330 69.77500 69.8250  0.95500
## 26   Oceania   1960 70.930 71.520 71.19750 71.1700  0.25250
## 27   Oceania   1970 71.890 73.490 72.38250 72.0750  0.61750
## 28   Oceania   1980 73.840 76.320 74.80500 74.5300  0.93500
## 29   Oceania   1990 76.330 78.830 77.56750 77.5550  0.63250
## 30   Oceania   2000 79.110 81.235 80.22975 80.2870  0.65575

From the table, we can see the life expectancy is increasing by decades for each continent. But the IQR for developing countries, like in Asia increase first and then decrease a littel bit. And for Europe and Americas, the IQR doesn’t change a lot, it’s steady.

How does GDP per capita vary by continent and by decade?

fun_gdp <- function(continent, decade) {
  
  a <- gapminder[which(gapminder$continent == continent & gapminder$decade == decade),]
  
  stats2 <- c(min = min(a$gdpPercap), max =max( a$gdpPercap),
             mean =  mean(a$gdpPercap), median = median(a$gdpPercap),
             IQR = quantile(a$gdpPercap, 0.75) - quantile(a$gdpPercap, 0.25))
  return(stats2)
}
fun_gdp("Africa",1950)
##       min       max      mean    median   IQR.75% 
##  298.8462 5487.1042 1318.9043 1024.0230  950.0916
gap <- data_by_decade(gapminder)

continentgdp <- c()
sta2<- c()
for(continent in levels(gap$continent)) {
  for(decade in levels(gap$decade)) {
    continentgdp <- rbind(continentgdp, c(continent, decade))
    sta2 <- rbind(sta2, fun_gdp(continent, decade ))
  }
}
continentgdp
##       [,1]       [,2]  
##  [1,] "Africa"   "1950"
##  [2,] "Africa"   "1960"
##  [3,] "Africa"   "1970"
##  [4,] "Africa"   "1980"
##  [5,] "Africa"   "1990"
##  [6,] "Africa"   "2000"
##  [7,] "Americas" "1950"
##  [8,] "Americas" "1960"
##  [9,] "Americas" "1970"
## [10,] "Americas" "1980"
## [11,] "Americas" "1990"
## [12,] "Americas" "2000"
## [13,] "Asia"     "1950"
## [14,] "Asia"     "1960"
## [15,] "Asia"     "1970"
## [16,] "Asia"     "1980"
## [17,] "Asia"     "1990"
## [18,] "Asia"     "2000"
## [19,] "Europe"   "1950"
## [20,] "Europe"   "1960"
## [21,] "Europe"   "1970"
## [22,] "Europe"   "1980"
## [23,] "Europe"   "1990"
## [24,] "Europe"   "2000"
## [25,] "Oceania"  "1950"
## [26,] "Oceania"  "1960"
## [27,] "Oceania"  "1970"
## [28,] "Oceania"  "1980"
## [29,] "Oceania"  "1990"
## [30,] "Oceania"  "2000"
sta2
##              min        max      mean    median    IQR.75%
##  [1,]   298.8462   5487.104  1318.904  1024.023   950.0916
##  [2,]   355.2032  18772.752  1824.221  1191.555  1104.8081
##  [3,]   464.0995  21951.212  2462.777  1402.376  1724.7493
##  [4,]   389.8762  17364.275  2382.131  1286.173  1983.9259
##  [5,]   312.1884  14722.842  2330.285  1179.883  2069.0063
##  [6,]   241.1659  13206.485  2844.209  1307.562  2826.6352
##  [7,]  1397.7171  14847.127  4347.553  3266.944  2205.0266
##  [8,]  1452.0577  19530.366  5284.898  4549.084  2799.6370
##  [9,]  1654.4569  24072.632  6921.671  5490.198  3124.1509
## [10,]  1823.0160  29884.350  7650.069  6397.723  4300.9379
## [11,]  1341.7269  35767.433  8467.118  6813.664  4848.2282
## [12,]  1201.6372  42951.653 10145.354  7382.469  5542.2684
## [13,]   331.0000 113523.133  5491.608  1497.727  2297.9368
## [14,]   349.0000  95458.112  5850.271  1852.401  4085.0329
## [15,]   357.0000 109347.867  7989.391  2850.723  8539.1421
## [16,]   385.0000  33693.175  7521.181  4106.509 11165.1958
## [17,]   347.0000  40300.620  9236.892  3685.722 15728.0982
## [18,]   611.0000  47306.990 11323.559  4287.633 18938.5251
## [19,]   973.5332  17909.490  6312.035  5730.677  4956.2819
## [20,]  1709.6837  22966.144  9254.655  8463.000  6811.9320
## [21,]  2860.1698  27195.113 13381.777 13044.562  8352.9485
## [22,]  3630.8807  31540.975 16416.104 15995.703  9922.0911
## [23,]  2497.4379  41283.164 18069.175 18122.048 16830.2216
## [24,]  4604.2117  49357.190 23383.107 26653.335 20705.2445
## [25,] 10039.5956  12247.395 10948.304 10753.113   846.7554
## [26,] 12217.2269  14526.125 13595.737 13819.798  1543.4051
## [27,] 16046.0373  18334.198 16850.645 16511.174   988.2239
## [28,] 17632.4104  21888.889 19501.375 19242.100  1416.4832
## [29,] 18363.3249  26997.937 22459.111 22237.590  3939.4177
## [30,] 23189.8014  34435.367 28374.483 27936.382  6938.4507
s2 <- data.frame(continentgdp,sta2)
names(s2)[1] <- "continent"
names(s2)[2] <- "decade"
s2
##    continent decade        min        max      mean    median    IQR.75.
## 1     Africa   1950   298.8462   5487.104  1318.904  1024.023   950.0916
## 2     Africa   1960   355.2032  18772.752  1824.221  1191.555  1104.8081
## 3     Africa   1970   464.0995  21951.212  2462.777  1402.376  1724.7493
## 4     Africa   1980   389.8762  17364.275  2382.131  1286.173  1983.9259
## 5     Africa   1990   312.1884  14722.842  2330.285  1179.883  2069.0063
## 6     Africa   2000   241.1659  13206.485  2844.209  1307.562  2826.6352
## 7   Americas   1950  1397.7171  14847.127  4347.553  3266.944  2205.0266
## 8   Americas   1960  1452.0577  19530.366  5284.898  4549.084  2799.6370
## 9   Americas   1970  1654.4569  24072.632  6921.671  5490.198  3124.1509
## 10  Americas   1980  1823.0160  29884.350  7650.069  6397.723  4300.9379
## 11  Americas   1990  1341.7269  35767.433  8467.118  6813.664  4848.2282
## 12  Americas   2000  1201.6372  42951.653 10145.354  7382.469  5542.2684
## 13      Asia   1950   331.0000 113523.133  5491.608  1497.727  2297.9368
## 14      Asia   1960   349.0000  95458.112  5850.271  1852.401  4085.0329
## 15      Asia   1970   357.0000 109347.867  7989.391  2850.723  8539.1421
## 16      Asia   1980   385.0000  33693.175  7521.181  4106.509 11165.1958
## 17      Asia   1990   347.0000  40300.620  9236.892  3685.722 15728.0982
## 18      Asia   2000   611.0000  47306.990 11323.559  4287.633 18938.5251
## 19    Europe   1950   973.5332  17909.490  6312.035  5730.677  4956.2819
## 20    Europe   1960  1709.6837  22966.144  9254.655  8463.000  6811.9320
## 21    Europe   1970  2860.1698  27195.113 13381.777 13044.562  8352.9485
## 22    Europe   1980  3630.8807  31540.975 16416.104 15995.703  9922.0911
## 23    Europe   1990  2497.4379  41283.164 18069.175 18122.048 16830.2216
## 24    Europe   2000  4604.2117  49357.190 23383.107 26653.335 20705.2445
## 25   Oceania   1950 10039.5956  12247.395 10948.304 10753.113   846.7554
## 26   Oceania   1960 12217.2269  14526.125 13595.737 13819.798  1543.4051
## 27   Oceania   1970 16046.0373  18334.198 16850.645 16511.174   988.2239
## 28   Oceania   1980 17632.4104  21888.889 19501.375 19242.100  1416.4832
## 29   Oceania   1990 18363.3249  26997.937 22459.111 22237.590  3939.4177
## 30   Oceania   2000 23189.8014  34435.367 28374.483 27936.382  6938.4507

The highest GDP per capita lies in developed countries, and their changes a smaller than Asia. But Asia has a great increase by decades.

Ryan

How does life expectancy vary by continent and by decade?

How does GDP per capita vary by continent and by decade?

Chris

How does life expectancy vary by continent and by decade?

Europe <- subset(gapminder, continent == "Europe")
Africa <- subset(gapminder, continent == "Africa")
Asia <- subset(gapminder, continent == "Asia")
Americas <- subset(gapminder, continent == "Americas")
Oceania <- subset(gapminder, continent == "Oceania")
aggregate(lifeExp~continent, gapminder, min)
##   continent lifeExp
## 1    Africa  23.599
## 2  Americas  37.579
## 3      Asia  28.801
## 4    Europe  43.585
## 5   Oceania  69.120
aggregate(lifeExp~continent, gapminder, max)
##   continent lifeExp
## 1    Africa  76.442
## 2  Americas  80.653
## 3      Asia  82.603
## 4    Europe  81.757
## 5   Oceania  81.235
aggregate(lifeExp~continent, gapminder, mean)
##   continent  lifeExp
## 1    Africa 48.86533
## 2  Americas 64.65874
## 3      Asia 60.06490
## 4    Europe 71.90369
## 5   Oceania 74.32621
aggregate(lifeExp~continent, gapminder, median)
##   continent lifeExp
## 1    Africa 47.7920
## 2  Americas 67.0480
## 3      Asia 61.7915
## 4    Europe 72.2410
## 5   Oceania 73.6650
aggregate(lifeExp~continent, gapminder, IQR)
##   continent lifeExp
## 1    Africa 12.0390
## 2  Americas 13.2895
## 3      Asia 18.0790
## 4    Europe  5.8805
## 5   Oceania  6.3475
filter(gapminder,(continent=="Europe" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Europe" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Europe" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Europe" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Europe" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Africa" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Africa" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Africa" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Africa" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Africa" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Asia" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Asia" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Asia" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Asia" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Asia" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Americas" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Americas" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Americas" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Americas" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Americas" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=lifeExp),binwidth=1)

How does GDP per capita vary by continent and by decade?

europe <- subset(gapminder, continent == "Europe")
africa <- subset(gapminder, continent == "Africa")
asia <- subset(gapminder, continent == "Asia")
americas <- subset(gapminder, continent == "Americas")
oceania <- subset(gapminder, continent == "Oceania")
aggregate(gdpPercap~continent, gapminder, min)
##   continent  gdpPercap
## 1    Africa   241.1659
## 2  Americas  1201.6372
## 3      Asia   331.0000
## 4    Europe   973.5332
## 5   Oceania 10039.5956
aggregate(gdpPercap~continent, gapminder, max)
##   continent gdpPercap
## 1    Africa  21951.21
## 2  Americas  42951.65
## 3      Asia 113523.13
## 4    Europe  49357.19
## 5   Oceania  34435.37
aggregate(gdpPercap~continent, gapminder, mean)
##   continent gdpPercap
## 1    Africa  2193.755
## 2  Americas  7136.110
## 3      Asia  7902.150
## 4    Europe 14469.476
## 5   Oceania 18621.609
aggregate(gdpPercap~continent, gapminder, median)
##   continent gdpPercap
## 1    Africa  1192.138
## 2  Americas  5465.510
## 3      Asia  2646.787
## 4    Europe 12081.749
## 5   Oceania 17983.304
aggregate(gdpPercap~continent, gapminder, IQR)
##   continent gdpPercap
## 1    Africa  1616.170
## 2  Americas  4402.431
## 3      Asia  7492.262
## 4    Europe 13248.301
## 5   Oceania  8072.258
filter(gapminder,(continent=="Europe" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Europe" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Europe" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Europe" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Europe" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Africa" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Africa" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Africa" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Africa" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Africa" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Asia" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Asia" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Asia" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Asia" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Asia" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Americas" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Americas" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Americas" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Americas" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Americas" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<1970 & year >= 1960)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<1980 & year >= 1970)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<1990 & year >= 1980)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<2000 & year >= 1990)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)

filter(gapminder,(continent=="Oceania" & year<2010 & year >= 2000)) %>%
  ggplot() +
  geom_freqpoly(aes(x=gdpPercap),binwidth=1)